Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden for behavioral disclosure. It states what the tool does but doesn't mention performance characteristics (e.g., whether it's optimized for counting vs. full scans), error handling, or any constraints like rate limits or permissions required for DynamoDB operations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with zero wasted words. It's appropriately sized and front-loaded with the essential information, making it highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 3 parameters, 100% schema coverage, and no output schema, the description is minimally adequate. It states the core purpose but lacks context about when to use it versus siblings, behavioral details, or output format, leaving gaps in completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds no additional meaning about parameters beyond implying filtering is possible, which is already covered in the schema. Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Count') and resource ('items in a DynamoDB table'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'scan-table' or 'query-table' which might also return counts, so it misses the top score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'scan-table' and 'query-table' that might offer similar functionality, there's no indication of when this specific counting tool is preferred or what its limitations are.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Get detailed information') but doesn't reveal critical traits like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or what 'detailed information' includes (e.g., schema, throughput, status). This leaves significant gaps for safe and effective tool invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple tool and front-loaded with the core action, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of DynamoDB operations and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'detailed information' entails (e.g., table properties, metrics), potential side effects, or error handling. For a tool interacting with a database system, more context is needed to ensure reliable use, especially with multiple sibling tools available.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the single parameter 'tableName' clearly documented as 'Name of the table to get details for'. The description doesn't add any semantic context beyond this, such as format constraints or examples. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, though no extra value is provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('detailed information about a DynamoDB table'), making the purpose understandable. However, it doesn't differentiate from siblings like 'list-tables' (which lists table names) or 'get-item' (which retrieves specific items), leaving some ambiguity about what 'detailed information' specifically entails compared to other table-related tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios like needing metadata vs. data retrieval, or how it differs from siblings such as 'list-tables' for table enumeration or 'get-item' for item-level details. Without such context, an agent might struggle to choose appropriately among the six sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but offers minimal behavioral context. It states the action but doesn't disclose permissions needed, error conditions, rate limits, or what happens if the key doesn't exist. For a database read operation, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It's appropriately sized and front-loaded with the core purpose, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a database read tool with no annotations and no output schema, the description is insufficient. It doesn't explain return values, error handling, or operational constraints. Given the complexity of DynamoDB operations and lack of structured metadata, more context is needed for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters fully. The description adds no additional meaning about parameter usage, constraints, or examples beyond what's in the schema. Baseline 3 is appropriate when the schema does all the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('an item from a DynamoDB table') with the specific mechanism ('based on a specific key'). It distinguishes from siblings like 'scan-table' or 'query-table' by emphasizing key-based retrieval, but doesn't explicitly contrast with all alternatives like 'paginate-query-table'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, when-not scenarios, or compare with sibling tools like 'query-table' for more complex retrievals or 'scan-table' for full table scans.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't cover critical aspects like whether it's read-only, requires authentication, has rate limits, or what the output format looks like (e.g., pagination, error handling). This leaves significant gaps for an agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with no wasted words, making it front-loaded and easy to parse. It efficiently conveys the core action without unnecessary elaboration, earning full marks for conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of DynamoDB operations and the lack of annotations and output schema, the description is incomplete. It doesn't explain behavioral traits, return values, or usage context, which are essential for an agent to operate this tool effectively in a real-world scenario.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. A baseline of 4 is applied as it adequately handles the lack of parameters without redundancy.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('list of all DynamoDB tables'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'describe-table' or 'scan-table' that also involve table operations, missing explicit distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. For example, it doesn't specify if this is for metadata listing versus data retrieval (like 'scan-table') or if there are prerequisites such as permissions. The description lacks context for selection among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but offers minimal behavioral context. It mentions 'paginate' which implies handling large result sets, but doesn't disclose pagination mechanics (e.g., token-based, limit parameters), rate limits, authentication needs, or what constitutes a 'query' in this context. The description doesn't contradict annotations since none exist.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise at just three words, with zero wasted language. It's front-loaded with the core functionality ('paginate query results') and contains no unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a pagination tool with 4 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what 'paginate' means operationally, how results are returned, error conditions, or relationship to the 'query-table' sibling. The minimal description leaves too many contextual gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, providing good documentation for all 4 parameters. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline for high schema coverage without compensating value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Paginate query results' states a general purpose but lacks specificity about what resource is being paginated (DynamoDB table query results) and how it differs from the sibling 'query-table' tool. It uses a clear verb ('paginate') but doesn't distinguish from alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus the 'query-table' sibling or other query/scan alternatives. The description implies pagination functionality but doesn't specify prerequisites, when pagination is needed, or when other tools might be more appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool queries items but doesn't mention whether it's read-only, if it has side effects, rate limits, authentication needs, or what the return format looks like. For a database query tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a DynamoDB query tool with 7 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain return values, error conditions, or behavioral constraints, leaving the agent with insufficient context to use the tool effectively beyond basic parameter passing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds no additional parameter semantics beyond implying 'conditions' relate to the filter and key condition expressions. This meets the baseline of 3 since the schema does the heavy lifting, but the description doesn't compensate with extra context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Query items') and resource ('from a DynamoDB table') with the purpose ('based on conditions'), making it easy to understand what the tool does. It distinguishes from siblings like 'scan-table' by specifying querying rather than scanning, though it doesn't explicitly differentiate from 'paginate-query-table' which might be a related operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'scan-table' or 'paginate-query-table'. It mentions conditions but doesn't specify that this is for key-based queries in DynamoDB, leaving the agent to infer usage from the tool name and parameters alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. 'Scan' implies a read operation, but it doesn't specify whether this is paginated, has performance implications for large tables, requires specific permissions, or what the output format looks like. For a DynamoDB operation with potential complexity, this leaves significant gaps in understanding tool behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise - a single sentence that directly states the tool's purpose without any unnecessary words. It's perfectly front-loaded and every word earns its place, making it efficient for an agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a DynamoDB scanning tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'scanning' means in DynamoDB context (sequential reads vs. query efficiency), doesn't mention performance considerations for large tables, and provides no information about return format or pagination behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all parameters are documented in the schema itself. The description doesn't add any meaningful parameter semantics beyond what's already in the schema - it doesn't explain relationships between parameters like how 'filterExpression' works with 'expressionAttributeValues' or typical scanning patterns. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('scan') and resource ('items from a DynamoDB table'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'query-table' or 'paginate-query-table' which might also retrieve data from DynamoDB tables, missing an opportunity for sibling distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'query-table' or 'get-item'. It doesn't mention typical use cases for scanning (e.g., full table reads vs. indexed queries) or any prerequisites, leaving the agent to infer usage from context alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.